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Usability testing method uncovers the problems in product or design using Participants activity into account and analysis with respect to identified use cases. A Usability Testing session can generate both qualitative and quantitative observation data which can processed further to reach on findings. The generated data from Usability Testing session is mainly dependent about the type of method and protocol chosen. If correct method and protocol is not chosen and executed, it may hamper the accuracy of data gathered and which may lead to inaccurate or biased findings. In this article, I would like to discuss about the type of Usability Testing methods, protocols and a small guide on when to use it. This would help you in understand and decide the method for better execution and accurate findings.

There are different ways to classify the Usability testing methods. When it comes to choose the Usability Testing method, one need to have answers of 3 parameters:

1. Context

2. Way

3. Protocol

1. With respect to Context

a. In-Person Usability Testing

In-person Usability Testing getting conducted in controlled or semi controlled testing environment. In that testing environment test Participant and Moderator will be present, apart from these actors some more roles can also be physically present like observer, note taker, business stake holders, project management team etc. Special environment setup is used for this kind of testing but not mandatory to have such infrastructure and setup. This can also be executed in any place with product, Moderator and Participant.

b. Remote Usability Testing

Remote usability testing is similar in fashion except one fact that is Participant and Moderator are not present at the same place instead this testing gets conducted by Participant alone and Moderator may or may not present through digital channels. This method is very impactful when the location or travel constraints are there in product lifecycle. Remote Usability Testing tools can be used in this method like Crazyegg, Maze, UserZoom etc.

2. With respect to Execution method

a. Moderated Usability Testing

This way of Usability Testing gets moderated by a Moderator or Facilitator who is expert in conducting & executing the studies, probing, observation skills etc. Moderator interacts with the Participants for explaining the tasks, probing and clarifications. The main goal of Moderator is to control the study and drive in correct directions. This method is amazingly useful in conducting Usability Testing in early design process and gather qualitative data which can help in making product more efficient by applying the insights as design iterations.

b. Un-moderated Usability Testing

On the contrary, unmoderated Usability Testing are not controlled by Moderators or facilitator. The test Participants are provided the list of tasks and testing materials. This is primarily used when the primary goal of Usability Testing is to measure some parameters and prove some hypothesis quantitatively. This kind of Usability Testing majorly done through online Usability Tools, which captures the Participants activity and provide the quantitative data analysis.

3. With respect to Protocol

Protocols define the way the Usability Testing session gets executed. The key activities are probing and think aloud, these activities are mainly configured or define with respect to the goals of observation. Let’s look the protocols in detail.

a. Concurrent probing

In this protocol, Moderator interacts with Participants while he/she performs the task and along-with the task goes. This method is good in clarifying granular level of details, whereas while measuring any quantitative parameter this way of execution may lead to erroneous results.

b. Retrospective probing

In this protocol, Moderator only observes the activity of Participant when the perform task and once the task(s) is completed Moderator performs the probing. In this way the Moderator does not interrupt the Participant in their task execution and measurable parameters gets more accurate values, whereas the Moderator might miss some key points to clarify or probe while waiting till the last.

c. Concurrent think aloud

In this protocol, the Moderator directs the Participants to think aloud their activities, decision making etc. while they perform the tasks. This is one of the best ways to capture the key qualitative insights about product from end user. Whereas, sometimes it may also lead into the direction of more discussion rather than actions. The Moderator should be skilled well to get the session execution is correct direction.

d. Retrospective think aloud

In this protocol, the Moderator directs the Participant to narrate his/her approach, decision making, activity etc. post the task execution. By this way the task attempt is well performed without interruption whereas, sometimes this method provides artificial data as compared to natural.

Conclusion

Usability Testing in design process is a structured and scientific method, which has many different ways to execute and solve different problems. Every way of execution solves different needs to product which makes it really important to choose the right method at right context. Conduction Usability Testing is good but conducting it in correct way is really important.

About the Author

What if I were to tell you that we can rig how consumers remember brand experiences; inducing positive memories and completely overriding negative ones?

Peak-End Rule

The peak-end rule is a psychological heuristic, or mental shortcut, that impacts how people remember past events.

For brands, it’s paramountto consider all of the cognitive biases that affect the customer decision process.

In understanding this, we can maximise our ability to establish deeper, richer connections with our consumers with less effort.

The science

The peak-end rule suggests that our brains simply cannot remember every detail of every day, we are limited in what information we can store.

Our brain shortcuts this process by condensing our experiences into a series of snapshots, consisting of the intense positive or negative moments (the “peaks”) and the final moments of an experience (the “end”).

Conceived by professors Kahneman & Tversky in 1999, several studies, conducted over an array of diverse circumstances, support the theory’s validity.

PEAK-END AT WORK

Creating peaks:

Television commercials that create positive feelings are rated more highly by viewers if there are peaks of intensity and end on a positive note, rather than a commercial that was consistently pleasant all the way through.

Holidays that ended on a bad flight home are remembered less favourably, despite everything on the holiday going well.

The perils of an unhappy ending:

A 2008 study demonstrated that college students who received a desirable gift, followed by a less desirable gift, were less happy than college students who only received the one desirable gift, despite receiving an extra gift.

Likewise, children were more pleased after receiving a chocolate treat alone, rather than a chocolate treat followed by a mildly enjoyable piece of gum.

Negative experiences are redeemable:

It’s no wonder women subject themselves to the pain of childbirth all over again, willingly, when their harrowing experiences end on such a rewarding high of a child.

A study proved that even a colonoscopy that had an extra 20 seconds, which were merely uncomfortable, not painful, added on to the end was rated better than those who did not receive the extra 20 seconds, despite being in discomfort for longer.

As a less gross example, patrons who received an otherwise negative dining experience, but were given a free dessert at the end, rated their experience considerably more favourably than those who did not.

“Manufacture the emotional peak purposefully, to create it by design.”

Adam Toporek, CX thought leader and author

ENTERPRISE SOFTWARE

Onboarding in software is a key opportunity to consider Peak-end. Modern apps like Slack focus on frictionless journeys which leave users feeling good about the experience.

In summary

Cognitive biases arbitrate entire segments of our memories so all that is left is how we felt during the peak and the end of the experience.

So how can we use this to our advantage?

Don’t be passive in allowing a consumer to meander through interactions with your platforms, be active; curate intentional designs devised to be affecting.

Brands need to design experiences for their consumers that will create an indelible impact. They will always remember the peaks and the end, so be exciting, surprising, and go out with a bang.

About the author

Anna Ryan is a strategist at Athlon working within research and insights. Her work helps create brand and product experiences that transform global brands and scale-ups.

Before starting the preparation of Usability Testing setup in Maze app, make sure that you have prepared Clickable prototype in Invision, list of features/use cases to be tested, prepared scenarios and tasks descriptions and identified your targeted participants.

Steps to
Setup Usability Testing in Maze App

Step 1 –
Create clickable prototype in Invision and copy the public link.

Step 2 – Import your prototype in Maze by pasting the prototype public URL and add New project.

Step 3 – Create Missions (Imagine missions as tasks that end user will need to perform in Usability Testing). You can create a list of Usability Testing tasks for users and then define the success path/Optimal path. Make sure you provide proper clickable points in prototype and correct success page (where the task should get completed). The recommended Missions/tasks count is 5-7 per Usability Test Session per Person, but it always depends upon the task length as well. Do not overload participants with tasks, it may lead to mental fatigue and lead to errors/biases.

Step 4 – Once you have created all of your missions (tasks), now you have reached to a stage where you can make your testing (Maze) live. By clicking ‘Send Live’ button at top-right corner, your Maze will be ‘live’ and you may get Usability Testing URL.

Step 5 – Always try and explore the testing URL and test setup by yourself. For this just become a test participant and go through the test using testing URL. If you find any changes in flow or any errors, correct them before reaching to actual participants.

Two years back, Toyota offered us a glimpse
into their version of the future where surprisingly, driving is still
fun. Concept-i is
the star in the autonomous future where people are still driving. And in the
case of Toyota, it's so much fun because they're cruising along with their
buddy Yui, an AI personality that helps them navigate, communicate and even
contributes in their discussions.

Yui is all over the car, controlling
every function and even taking the wheel when required to. It's definitely an
exciting future where the machine sounds and “feels” like a human, even
exhibiting empathetic behaviour.

Related: Preparing for the Future of AI

That's the kind of future I'd imagine awaits
user experience (UX) in the world of AI. A time when the human-AI connection is
so deep that some experts say there will be “no interface.” But currently, UX
does depend on an interface. It requires screens, for instance, and they don't
do much justice to it. Integrating AI into the process will mean better
experience all around.

From websites to homes and cars, here's how
AI could help patch the holes and bring UX closer to maximum potential.

1.
Complex data analysis.

Until now, to improve user engagement in
their products, UX teams have turned to tools and metrics such as usability
tests, A/B tests, heat maps and usage data. However, these methods are
soon to be eclipsed by AI. It's not so much because AI can collect more data --
it's how much you can do with it.

Using AI, an ecommerce store can track user behaviour
across various platforms to provide the owner with tips on how they can improve
their purchasing experience, eventually leading to more sales. AI can be used
to tailor the design to each user’s specifications, based on the analysis of
the collected data.

All this is achieved through the application of deep
learning that combines large data sets to make inferences. Additionally, these
systems can learn from the data and adjust their behaviour accordingly, in real
time. Thus, designers applying AI in their work are likely to create better UIs
at a faster rate.

2. Deeper human connection.

By analysing the vast amount of data
collected, AI systems can create a deeper connection with humans, enhancing
their relationship. This is already happening in a couple of industries. When
you think of Siri, you see a friendly-voiced (digital) personal assistant.
When Amazon first introduced Alexa, it took the market by storm. But its
usefulness could only be proven over time. And it was. Smart-home owners are
using it to do a million things, including scouring the internet for recipes,
schedule meetings and shop. It's also being used in ambulances. Even Netflix’s highly predictive algorithm is
a case example of AI in use.

Toyota says Concept-i isn't just a car, but a
partner. From the simulation video, you can see that Yui connects with the
family on a level that current UX doesn't reach.

By using the function over and over,
consumers end up establishing an interdependent relationship with the system.
That's exactly how AI is designed to work. You use the system; it collects
data; it uses it to learn; it becomes more useful; gives better user
experience; you use it more as it collects data, learns and becomes more
useful; and the cycle continues. You don't even see it coming -- and before you
know it, you're deeply connected.

3. More control by the user.

A common concern about the adoption of AI to
everyday life is whether the machines might eventually rise and take over the
world. In other words, users are concerned about losing control over the
systems. It's a legitimate
concern with the autonomous cars, robots guards
and smart homes expected to become commonplace.

This lack of control is mirrored in the
skepticism for the future, but it can also be seen in commerce and other areas
where user experience is of great importance. For instance, a user will be more
likely to enter their card information into a system if they feel they have
control over when money is transferred, to whom it goes and that they can
retrieve it in case something goes wrong.

As AI develops, users will gain more control over the system, gradually improving trust which will lead to more usage.

In Which AI Could Enhance Your Company's UX

UX design is about a designer trying to communicate a machine's model to the user. Meaning, the designer is trying to show the user how the machine works and the kind of benefits they can get from it, from the former's point of view.

Traditionally, this involved following certain rules, and designers understood them very well. A designer knows how to create a web page by following certain rules that they can probably manipulate. With AI, however, the design is dependent on a complex analysis of data instead of following sets of rules. To be able to design using AI, designers will have to really understand the technology behind it.

Mixing UX and AI as we can have played with “AIBO”

Artificial intelligence

Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with
intelligent beings. The term is frequently applied to the project of developing
systems endowed with the intellectual processes characteristic of humans, such as the
ability to reason, discover meaning, generalize, or learn from past experience.
Since the development of the digital
computer in the 1940s, it has been demonstrated
that computers can be programmed to carry out very complex tasks - as, for
example, discovering proofs for mathematical theorems or playing chess - with great proficiency. Still, despite continuing
advances in computer processing speed and memory capacity, there are as yet no
programs that can match human flexibility over wider domains or in tasks
requiring much everyday knowledge. On the other hand, some programs have
attained the performance levels of human experts and professionals in
performing certain specific tasks, so that artificial intelligence in this
limited sense is found in applications as diverse as medical diagnosis, computer search
engines, and voice or handwriting recognition.

What
Is Intelligence?

All but the simplest human behaviour is
ascribed to intelligence, while even the most complicated insect behaviour
is never taken as an indication of intelligence. What is the difference?
Consider the behaviour of the digger wasp, Sphex
ichneumoneus. When the female wasp returns to her burrow with food,
she first deposits it on the threshold,
checks for intruders inside her burrow, and only then, if the coast is clear,
carries her food inside. The real nature of the wasp’s instinctual
behaviour is revealed if the food is moved a few inches
away from the entrance to her burrow while she is inside: on emerging, she will
repeat the whole procedure as often as the food is displaced.

Fixing
the AI in real time

Problem solving, particularly in artificial intelligence, may be characterized as a systematic search through a range of possible actions in order to reach some predefined goal or solution. Problem-solving methods divide into special purpose and general purpose. A special-purpose method is tailor-made for a particular problem and often exploits very specific features of the situation in which the problem is embedded. In contrast, a general-purpose method is applicable to a wide variety of problems. One general-purpose technique used in AI is means-end analysis—a step-by-step, or incremental, reduction of the difference between the current state and the final goal. The program selects actions from a list of means—in the case of a simple robot this might consist of PICKUP, PUTDOWN, MOVEFORWARD, MOVEBACK, MOVELEFT, and MOVERIGHT—until the goal is reached.Many diverse problems have been solved by
artificial intelligence programs. Some examples are finding the winning move
(or sequence of moves) in a board game, devising mathematical proofs, and
manipulating “virtual objects” in a computer-generated world.